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                <p>代码中涉及的知识点<span id="more"></span></p>
<h1 id="Data-py中"><a href="#Data-py中" class="headerlink" title="Data.py中"></a><strong>Data.py</strong>中</h1><h3 id="一、update-tag-seq函数"><a href="#一、update-tag-seq函数" class="headerlink" title="一、update_tag_seq函数"></a>一、update_tag_seq函数</h3><pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">def</span> <span class="token function">update_tag_seq</span><span class="token punctuation">(</span>em_text<span class="token punctuation">,</span> sentence_text<span class="token punctuation">,</span> relation_label<span class="token punctuation">,</span> role<span class="token punctuation">,</span> tag_set<span class="token punctuation">,</span> tags_idx<span class="token punctuation">)</span>
 <span class="token comment" spellcheck="true"># 示例句子：{"sentText": "But that spasm of irritation by a master intimidator was minor compared with what Bobby Fischer , the erratic former world chess champion , dished out in March at a news conference in Reykjavik , Iceland .", "articleId": "/m/vinci8/data1/riedel/projects/relation/kb/nyt1/docstore/nyt-2005-2006.backup/1677367.xml.pb", "relationMentions": [{"em1Text": "Bobby Fischer", "em2Text": "Iceland", "label": "/people/person/nationality"}, {"em1Text": "Iceland", "em2Text": "Reykjavik", "label": "/location/country/capital"}, {"em1Text": "Iceland", "em2Text": "Reykjavik", "label": "/location/location/contains"}, {"em1Text": "Bobby Fischer", "em2Text": "Reykjavik", "label": "/people/deceased_person/place_of_death"}], "entityMentions": [{"start": 0, "label": "PERSON", "text": "Bobby Fischer"}, {"start": 1, "label": "LOCATION", "text": "Reykjavik"}, {"start": 2, "label": "LOCATION", "text": "Iceland"}], "sentId": "1"}</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<p><strong>参数详解：</strong></p>
<ul>
<li>em_text:分割后的实体的集合。例如：[‘Bobby’,’Fischer’]。</li>
<li>sentence_text:一个句子分割成单个单词后的集合。 [‘But’, ‘that’, ‘spasm’, ‘of’, ‘irritation’, ‘by’, ‘a’, ‘master’, ‘intimidator’, ‘was’, ‘minor’, ‘compared’, ‘with’, ‘what’, ‘Bobby’, ‘Fischer’, ‘,’, ‘the’, ‘erratic’, ‘former’, ‘world’, ‘chess’, ‘champion’, ‘,’, ‘dished’, ‘out’, ‘in’, ‘March’, ‘at’, ‘a’, ‘news’, ‘conference’, ‘in’, ‘Reykjavik’, ‘,’, ‘Iceland’, ‘.’]</li>
</ul>
<h1 id="Train-py中"><a href="#Train-py中" class="headerlink" title="Train.py中"></a><strong>Train.py</strong>中</h1><h3 id="一、for-in-range-20-中’-’的意思"><a href="#一、for-in-range-20-中’-’的意思" class="headerlink" title="一、for _ in range(20)中’_’的意思"></a>一、for _ in range(20)中’_’的意思</h3><p><strong>简述：</strong>‘_’标志只是一个循环标志，也可以用i,j替换，在循环中不会用到。只是一个循环的次数。相当 与c语言中的for(i = 0;i ++;i&lt;1000)</p>
<pre class="line-numbers language-python"><code class="language-python">a <span class="token operator">=</span> <span class="token number">0</span>
b <span class="token operator">=</span> <span class="token number">1</span>
<span class="token keyword">for</span> _ <span class="token keyword">in</span> range<span class="token punctuation">(</span><span class="token number">20</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token punctuation">(</span>a<span class="token punctuation">,</span> b<span class="token punctuation">)</span> <span class="token operator">=</span> <span class="token punctuation">(</span>b<span class="token punctuation">,</span> a <span class="token operator">+</span> b<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p>输出结果为：</p>
<p><img src="/Images/Jointextraction/image-20210410160948581.png" alt="image-20210410160948581"></p>
<p><img src="/Images/jointextraction/image-20210621181427927.png" alt="image-20210621181427927"></p>
<h3 id="二、python-uniform-函数"><a href="#二、python-uniform-函数" class="headerlink" title="二、python uniform()函数"></a>二、python uniform()函数</h3><p>**简述:**uniform函数将随机（需要导入random函数）生成下一个实数，它在[x,y]范围内。</p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">import</span> random
a <span class="token operator">=</span> random<span class="token punctuation">.</span>uniform<span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token number">4</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span></span></code></pre>
<p>输出结果为：<br><img src="/Images/jointextraction/image-20210411193413163.png" alt="image-20210411193413163"></p>
<p>注：生成的数会有小数，所以如需整数，则改为a = int(random.uniform(3,4))</p>
<h3 id="三、unk-和-pad的作用"><a href="#三、unk-和-pad的作用" class="headerlink" title="三、unk 和 pad的作用"></a>三、unk 和 pad的作用</h3><p><strong>简述：</strong>是为了保证数据的输入格式一致。</p>
<p><strong>数据的输入格式：（以中文为例）</strong></p>
<ul>
<li>分词后去除停止词，再对词语进行词频统计，取频数最高的前6000个词语作为词汇表。</li>
<li>想词汇表中加入pad和unk，实际的词汇表的词语总数为6000+2=6002；</li>
<li>当句子长度大于指定的最大长度，进行裁剪，小于最大长度，在句子前面用pad进行填充。</li>
<li>如果句子中的词语在词汇表中没有出现则用unk代替。</li>
<li>输入到网络中的句子实数是进行分词后的词语映射的id。</li>
<li>输入的标签是要进行编码的。</li>
</ul>
<h3 id="四、optimizer"><a href="#四、optimizer" class="headerlink" title="四、optimizer"></a>四、optimizer</h3><p><strong>简述：</strong>optimizer是深度学习模型中非常重要的模块，它决定参数更新的方向，快慢和大小。好的optimizer算法</p>
<p>合适的参数使得模型又快又准。</p>
<h3 id="五、sorted-函数"><a href="#五、sorted-函数" class="headerlink" title="五、sorted()函数"></a>五、sorted()函数</h3><p><strong>简述</strong>：sorted()函数是对所有可迭代的对象进行排序操作。</p>
<p><strong>函数形式：</strong>sorted(iterable, cmp=None, key=None, reverse=False)</p>
<p><strong>参数讲解：</strong></p>
<ul>
<li><code>iterable</code>: 可迭代的对象。</li>
<li><code>cmp</code>: 比较的函数，这个具有两个参数，参数的值都是从可迭代对象中取出，此函数必须遵守的规则为，大于则返回1，小于则返回-1，等于则返回0。</li>
<li><code>key</code>: 主要是用来进行比较的元素，只有一个参数，具体的函数的参数就是取自于可迭代对象中，指定可迭代对象中的一个元素来进行排序。</li>
<li><code>reverse</code>: 排序规则，reverse = True 降序 ， reverse = False 升序（默认）</li>
</ul>
<h3 id="六、Sampler类与四种采样方式"><a href="#六、Sampler类与四种采样方式" class="headerlink" title="六、Sampler类与四种采样方式"></a>六、Sampler类与四种采样方式</h3><p><strong>简述：</strong>由于不能将大量数据一次性放入网络中进行训练，所以需要分批进行数据的读取，则涉及到如何从数据集<br>读取的问题，pytorch提供了sampler与多个子类实现不同方式的数据采样。子类采样有四种：</p>
<ul>
<li>sequential Sampler(顺序采样)</li>
<li>Random Sampler(随机采样)</li>
<li>Subset Random Sampler(子集随机采样)</li>
<li>Weighted Random Sampler(加权随机采样)</li>
<li>Batch Sampler</li>
</ul>
<p>本文仅涉及BatchSampler及SubsetRandomSampler：</p>
<p>&emsp;&emsp;<code>SubsetRandomSampler</code>:这其中，返回的不是随机数序列，而是通过随机数序列作为indices(切片)的索引，进而<strong>返回打乱的数据本身</strong>。即返回的不是索引，而是数据本身。采样是不重复的。如data = list([17, 22, 3, 41, 8]),sub_sampler_train = sampler.SubsetRandomSampler(indices=data[0:2])。则最终输出为：[17,22]。</p>
<p>&emsp;&emsp;<code>BatchSampler</code>:在定义好各种采样器以后，需要进行“batch”的采样。有三个参数，sampler,batch_size.drop_last。<strong>drop_last为“True”时，如果采样得到的数据个数小于batch_size则抛弃本个batch的数据</strong>。<strong>drop_last为False的情况下，for循环就会yield出个batch</strong>。</p>
<pre class="line-numbers language-python3"><code class="language-python3">       # 定义使用何种采样器Sampler
        self.sampler = sampler
        self.batch_size = batch_size
     # 是否在采样个数小于batch_size时剔除本次采样
        self.drop_last = drop_last
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>两种函数结合</strong>源代码示例：</p>
<pre class="line-numbers language-python"><code class="language-python">BatchSampler<span class="token punctuation">(</span>SubsetRandomSampler<span class="token punctuation">(</span>data_group<span class="token punctuation">.</span>indices<span class="token punctuation">)</span><span class="token punctuation">,</span>batch_size<span class="token punctuation">,</span> drop_last<span class="token operator">=</span>false<span class="token punctuation">)</span> <span class="token comment" spellcheck="true">#batch_size = 32 </span>
<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<p><strong>代码说明</strong>：首先subsetrandomsampler函数从data_group.indices中随机抽取数据，之后batchsampler对抽取的数字以32的batch_size截取抽取的数据。</p>
<h3 id="七、python-zip-函数"><a href="#七、python-zip-函数" class="headerlink" title="七、python zip()函数"></a>七、python zip()函数</h3><p><strong>简述：</strong>zip()函数是用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。</p>
<p><strong>示例如下：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token operator">>></span><span class="token operator">></span>a <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span><span class="token number">2</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">]</span>
<span class="token operator">>></span><span class="token operator">></span> b <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span><span class="token number">5</span><span class="token punctuation">,</span><span class="token number">6</span><span class="token punctuation">]</span>
<span class="token operator">>></span><span class="token operator">></span> c <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span><span class="token number">5</span><span class="token punctuation">,</span><span class="token number">6</span><span class="token punctuation">,</span><span class="token number">7</span><span class="token punctuation">,</span><span class="token number">8</span><span class="token punctuation">]</span>
<span class="token operator">>></span><span class="token operator">></span> zipped <span class="token operator">=</span> zip<span class="token punctuation">(</span>a<span class="token punctuation">,</span>b<span class="token punctuation">)</span>     <span class="token comment" spellcheck="true"># 打包为元组的列表</span>
<span class="token punctuation">[</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">)</span><span class="token punctuation">]</span>
<span class="token operator">>></span><span class="token operator">></span> zip<span class="token punctuation">(</span>a<span class="token punctuation">,</span>c<span class="token punctuation">)</span>              <span class="token comment" spellcheck="true"># 元素个数与最短的列表一致</span>
<span class="token punctuation">[</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">)</span><span class="token punctuation">]</span>
<span class="token operator">>></span><span class="token operator">></span> zip<span class="token punctuation">(</span><span class="token operator">*</span>zipped<span class="token punctuation">)</span>          <span class="token comment" spellcheck="true"># 与 zip 相反，*zipped 可理解为解压，返回二维矩阵式</span>
<span class="token punctuation">[</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">)</span><span class="token punctuation">]</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>注：</strong>zip在python2与python3中的返回值不同，上面是Python2中的返回，如果是Python3，则返回的是&lt;zip object at 0x0000022F69896D48&gt;</p>
<h3 id="八、pytorch中序列填充"><a href="#八、pytorch中序列填充" class="headerlink" title="八、pytorch中序列填充"></a>八、pytorch中序列填充</h3><h3 id="九、梯度裁剪"><a href="#九、梯度裁剪" class="headerlink" title="九、梯度裁剪"></a>九、梯度裁剪</h3><p><strong>简述：</strong>在神经网络训练过程中，循环神经网络可以利用过去输入的隐藏状态在序贯上表现非常好，但是这种架构存在两个问题：梯度爆炸（模型训练过程中梯度变得太大使得模型不稳定）和梯度消失（有可能使得训练过程无法继续）。所以需要梯度裁剪处理梯度爆炸的问题。</p>
<p>后续时间更新：<a target="_blank" rel="noopener" href="https://blog.csdn.net/qq_34769162/article/details/111171116">https://blog.csdn.net/qq_34769162/article/details/111171116</a><br><a target="_blank" rel="noopener" href="https://blog.csdn.net/qq_41800366/article/details/86583789">https://blog.csdn.net/qq_41800366/article/details/86583789</a></p>
<h3 id="十、total-loss-loss-item-解释"><a href="#十、total-loss-loss-item-解释" class="headerlink" title="十、total_loss += loss.item() 解释"></a>十、total_loss += loss.item() 解释</h3><p>简述：一个epochs里也是按照很多个batchs进行训练。所以需要把一个epochs里的每次的batchs的loss加起来，等这一个epochs训练完后，会把累加的loss除以batchs的数量，得到这个epochs的损失。</p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">for</span> epoch <span class="token keyword">in</span> range<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> args<span class="token punctuation">.</span>epochs<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">for</span> idx<span class="token punctuation">,</span> batch_indices <span class="token keyword">in</span> enumerate<span class="token punctuation">(</span>sampler<span class="token punctuation">)</span><span class="token punctuation">:</span>
       total_loss <span class="token operator">+=</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>
    cur_loss <span class="token operator">=</span> total_loss <span class="token operator">/</span> count
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span></span></code></pre>
<h3 id="十一、学习率的调节（后期增加学习https-www-pianshen-com-article-6347985691-）"><a href="#十一、学习率的调节（后期增加学习https-www-pianshen-com-article-6347985691-）" class="headerlink" title="十一、学习率的调节（后期增加学习https://www.pianshen.com/article/6347985691/）"></a>十一、学习率的调节（后期增加学习<a target="_blank" rel="noopener" href="https://www.pianshen.com/article/6347985691/%EF%BC%89">https://www.pianshen.com/article/6347985691/）</a></h3><p><strong>简述：</strong>学习率对于深度学习是一个重要的超参数，它控制着基于损失梯度调整神经网络权值的速度，在很多优化算法中（SGD、Adm）对其有所涉及。学习率越小，损失梯度下降的速度越慢，收敛时间更长。很大的话，梯度下降的步子过大可能会跨过最优值。</p>
<p><strong>学习率的调整：</strong></p>
<ul>
<li><p>根据数据集的大小来选择合适的学习率，当使用平方和误差作为成本函数时，随着数据量的增多，学习率应该被设置为相应更小的值（从梯度下降算法的原理可以分析得出）。另一种方法就是，选择不受数据集大小影响的成本函数-均值平方差函数。</p>
</li>
<li><p>训练全过程并不是使用一个固定值的学习速度，而是随着时间的推移让学习率动态变化，比如刚开始训练，离下山地点的最优值还很远，那么可以使用较大的学习率下的快一点，当快接近最优值时为避免跨过最优值，下山速度要放缓，即应使用较小学习率训练，具体情况下因为我们也不知道训练时的最优值，所以具体的解决办法是：在每次迭代后，使用估计的模型的参数来查看误差函数的值，如果相对于上一次迭代，错误率减少了，就可以增大学习率如果相对于上一次迭代，错误率增大了，那么应该重新设置上一轮迭代的值，并且减少学习率到之前的50%。</p>
<h3 id="十二、with-torch-no-grad"><a href="#十二、with-torch-no-grad" class="headerlink" title="十二、with torch.no_grad()"></a>十二、with torch.no_grad()</h3></li>
</ul>
<p><strong>简述：</strong>pytorch中不是所有的操作都需要进行计算图的生成（计算过程的构建，以便梯度反向传播等梯度）。在对于tensor的计算操作中，默认是要进行计算图的构建的，在这种情况下，可以使用with torch.no_grad（）。在TensorFlow中，使用with tf.session as sess，开启计算图的对话。</p>
<ul>
<li>使用with torch.no_grad（）</li>
</ul>
<p><img src="/Images/jointextraction/image-20210414160143901.png" alt="image-20210414160143901"></p>
<ul>
<li><p>不使用with torch.no_grad（）</p>
<p><img src="/Images/jointextraction/image-20210414161323446.png" alt="image-20210414161323446"></p>
</li>
</ul>
<p>通过比较可得：不使用with torch.no_grad()会有grad_fn这个属性，这表示计算的结果在计算图当中可以进行梯度反传（反向传播）等操作，其他方面没什么区别。</p>
<h3 id="十三、pad-pack-sequence以及pack-pad-sequence"><a href="#十三、pad-pack-sequence以及pack-pad-sequence" class="headerlink" title="十三、pad_pack_sequence以及pack_pad_sequence"></a>十三、pad_pack_sequence以及pack_pad_sequence</h3><h3 id="十四、torch-argmax-input-dim-None-keepdim-False-函数"><a href="#十四、torch-argmax-input-dim-None-keepdim-False-函数" class="headerlink" title="十四、torch.argmax(input,dim=None,keepdim=False)函数"></a>十四、torch.argmax(input,dim=None,keepdim=False)函数</h3><p><strong>简述：</strong>此函数根据dim决定找到某一行或某一列的最大值，最终返回最大值所在<strong>位置的索引</strong>。</p>
<p>示例：</p>

                
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